iMiGUE collected 359 videos (258 wins and 101 losses) of post match press conferences of Grand Slam tournaments from online video sharing platforms, e.g., YouTube, of the total length of 2 092 minutes. The videos' duration varies with an average length of 350 seconds. The videos' resolution is 1280\(\times \) 720, and their frame rate is 25 fps. A total of 18 499 MG samples were labeled out and assigned with 32 category labels, i.e., about 51 MG samples each video on average. The length of MG instances also varies, from 0.18s (second) to 80.92s with an average duration of 2.55s. Table REF  shows the key characteristics numbers of the iMiGUE compares with other gesture datasets. Notice that the sample numbers of the 32 MG categories vary a lot (see Fig. REF  (b)), which is a common situation in many spontaneous emotion datasets [1]}, [2]}, [3]} as it is not control-recorded data and the occurrence of different behaviors naturally varies. The sample unbalance is one challenge for MG recognition, which we will elaborate later in Sec. .
